Developing and Implementing Peer-Led Intervention to Support Staff in Long-Term Care Homes Manage Grief
Bibliographic record
Abstract
Front-line staff in long-term care (LTC) homes often form strong emotional bonds with residents. When residents die, staffs’ grief often goes unattended, and may result in disenfranchised grief. The aim of this article is to develop, implement, and assess the benefits of a peer-led debriefing intervention to help staff manage their grief and provide LTC homes an organizational approach to support them. This research was nested within a 5-year participatory action research to develop and implement palliative care programs within four LTC homes in Canada. Data specific to this debriefing intervention included questionnaires from six peer debriefers, field observations of six debriefings, and qualitative interviews with 23 staff participants. An original peer-led debriefing intervention (INNPUT) for LTC home staff was developed and implemented. Data revealed that the intervention offered staff an opportunity to express grief in a safe context with others, an opportunity for closure and acknowledgment. The INNPUT intervention benefits staff and offers an innovative, sustainable, easy to use strategy for LTC homes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".